Predicting BRAF Mutations in Cutaneous Melanoma Patients Using Neural Network Analysis

Area of Science:

• Oncology
• Genetics
• Dermatology

Background:

• Point mutations in the BRAF oncogene are common in cutaneous melanoma (CM).
• BRAF mutation status is crucial for personalized CM patient management.
• Molecular testing for BRAF mutations can be costly and inaccessible in some regions.

Purpose of the Study:

• To develop a predictive model for BRAF gene alterations in CM.
• To utilize routinely available clinical and histological data for prediction.
• To support personalized treatment decisions for CM patients.

Main Methods:

• A cohort of 2041 CM patients was analyzed.
• Key clinical and histological variables were assessed, including age, location, subtype, ulceration, and invasion.
• A multilayer perceptron (MLP) neural network model was developed and validated.

Main Results:

• The MLP model achieved an AUROC of 0.79, demonstrating good predictive performance.
• Key predictors for BRAF mutations included patient age, tumor location, histological type, lymphovascular invasion, ulceration, and nevus association.
• The model showed a sensitivity of 89.4% and specificity of 50.7% at the optimal threshold.

Conclusions:

• A validated MLP model can accurately predict BRAF mutation status in CM patients.
• The model relies on six easily accessible clinical and histological variables.
• This approach can assist in guiding personalized management strategies for CM, particularly in resource-limited settings.